Null Hypothesis Significance Testing: Ramifications, Ruminations and Recommendations

2005 ◽  
Vol 35 (1) ◽  
pp. 1-20 ◽  
Author(s):  
G. K. Huysamen
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Population Parameter ◽  
Size Estimation ◽  
Null Hypothesis Significance Testing ◽  
Point Estimates ◽  
Size Estimates

Criticisms of traditional null hypothesis significance testing (NHST) became more pronounced during the 1960s and reached a climax during the past decade. Among others, NHST says nothing about the size of the population parameter of interest and its result is influenced by sample size. Estimation of confidence intervals around point estimates of the relevant parameters, model fitting and Bayesian statistics represent some major departures from conventional NHST. Testing non-nil null hypotheses, determining optimal sample size to uncover only substantively meaningful effect sizes and reporting effect-size estimates may be regarded as minor extensions of NHST. Although there seems to be growing support for the estimation of confidence intervals around point estimates of the relevant parameters, it is unlikely that NHST-based procedures will disappear in the near future. In the meantime, it is widely accepted that effect-size estimates should be reported as a mandatory adjunct to conventional NHST results.

scholarly journals Null Hypothesis Significance Testing: a short tutorial

2015 ◽  
Author(s):  
Cyril R Pernet
Keyword(s):  
Social Sciences ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Good Practices ◽  
Interpretation Errors ◽  
Statistical Issues ◽  
Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

Invasive Plant Researchers Should Calculate Effect Sizes, Not P-Values

2010 ◽  
Vol 3 (2) ◽  
pp. 106-112 ◽  
Author(s):  
Matthew J. Rinella ◽  
Jeremy J. James
Keyword(s):  
Confidence Intervals ◽  
Null Hypothesis ◽  
Invasive Plant ◽  
Effect Sizes ◽  
Significance Testing ◽  
Research Articles ◽  
Plant Science ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Size Estimates

AbstractNull hypothesis significance testing (NHST) forms the backbone of statistical inference in invasive plant science. Over 95% of research articles in Invasive Plant Science and Management report NHST results such as P-values or statistics closely related to P-values such as least significant differences. Unfortunately, NHST results are less informative than their ubiquity implies. P-values are hard to interpret and are regularly misinterpreted. Also, P-values do not provide estimates of the magnitudes and uncertainties of studied effects, and these effect size estimates are what invasive plant scientists care about most. In this paper, we reanalyze four datasets (two of our own and two of our colleagues; studies put forth as examples in this paper are used with permission of their authors) to illustrate limitations of NHST. The re-analyses are used to build a case for confidence intervals as preferable alternatives to P-values. Confidence intervals indicate effect sizes, and compared to P-values, confidence intervals provide more complete, intuitively appealing information on what data do/do not indicate.

scholarly journals Null Hypothesis Significance Testing: a short tutorial

2015 ◽  
Author(s):  
Cyril R Pernet
Keyword(s):  
Social Sciences ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Good Practices ◽  
Interpretation Errors ◽  
Statistical Issues ◽  
Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

scholarly journals Null Hypothesis Significance Testing: a short tutorial

2015 ◽  
Author(s):  
Cyril R Pernet
Keyword(s):  
Social Sciences ◽  
Confidence Intervals ◽  
Effect Size ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Good Practices ◽  
Interpretation Errors ◽  
Statistical Issues ◽  
Bayesian Factor

Although thoroughly criticized, null hypothesis significance testing is the statistical method of choice in biological, biomedical and social sciences to investigate if an effect is likely. In this short tutorial, I first summarize the concepts behind the method while pointing to common interpretation errors. I then present the related concepts of confidence intervals, effect size, and Bayesian factor, and discuss what should be reported in which context. The goal is to clarify concepts, present statistical issues that researchers face using the NHST framework and highlight good practices.

scholarly journals A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow
Keyword(s):  
Present Article ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
A Priori ◽  
Significance Testing ◽  
Population Parameters ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Statistical Procedures ◽  
Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

The Numbers Will Love You Back in Return—I Promise

2016 ◽  
Vol 11 (4) ◽  
pp. 551-554 ◽  
Author(s):  
Martin Buchheit
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Clinical Medicine ◽  
Statistical Significance ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Sport Science ◽  
Size Dependent ◽  
Research Questions ◽  
Per Se

The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.

Confidence Intervals

2009 ◽  
Vol 217 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Geoff Cumming ◽  
Fiona Fidler
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Prediction Intervals ◽  
Effect Sizes ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Standardized Effect Sizes ◽  
Mainstream Science

Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions – those that demand quantitative answers – and using CIs to answer them. They explain CIs and a range of ways to think about them and use them to interpret data, especially by considering CIs as prediction intervals, which provide information about replication. They explain how to calculate CIs on means, proportions, correlations, and standardized effect sizes, and illustrate symmetric and asymmetric CIs. They also argue that information provided by CIs is more useful than that provided by p values, or by values of Killeen’s prep, the probability of replication.

scholarly journals Degrees of Corroboration: An Antidote to the Replication Crisis

2019 ◽  
Author(s):  
Jan Sprenger
Keyword(s):  
Bayesian Inference ◽  
Statistical Inference ◽  
Confidence Intervals ◽  
Publication Bias ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Epistemic Authority ◽  
Hypothesis Tests ◽  
Null Hypothesis Significance Testing ◽  
Replication Crisis

The replication crisis poses an enormous challenge to the epistemic authority of science and the logic of statistical inference in particular. Two prominent features of Null Hypothesis Significance Testing (NHST) arguably contribute to the crisis: the lack of guidance for interpreting non-significant results and the impossibility of quantifying support for the null hypothesis. In this paper, I argue that also popular alternatives to NHST, such as confidence intervals and Bayesian inference, do not lead to a satisfactory logic of evaluating hypothesis tests. As an alternative, I motivate and explicate the concept of corroboration of the null hypothesis. Finally I show how degrees of corroboration give an interpretation to non-significant results, combat publication bias and mitigate the replication crisis.

scholarly journals Ο έλεγχος μηδενικών υποθέσεων: διαδικασία, παρανοήσεις και μερικές προτάσεις για καλύτερες πρακτικές

2020 ◽  
Vol 18 (2) ◽  
pp. 224
Author(s):  
Πέτρος Ρούσσος
Keyword(s):  
Confidence Intervals ◽  
Null Hypothesis ◽  
Research Data ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Research Results ◽  
Error Bars ◽  
Reform Debate

The rationale of Null Hypothesis Significance Testing (NHST) is described, and the consequences of its hybridism are discussed. The paper presents examples published in “PSYCHOLOGY: The Journal of the HPS” refer to NHST and interpret its outcomes. We examined the 445 articles published between 1992 and 2010. We noted misuses of NHST and searched for any use of confidence intervals or error bars or use of these to support interpretation. Part of the paper focuses on the statistical-reform debate and provides detailed guidance about good statistical practices in the analysis of research data and the interpretation of findings. The proposed guide does not fall into the trap of mandating the use of particular procedures; it rather aims to support readers’ understanding of research results.

scholarly journals Null hypothesis significance testing: a guide to commonly misunderstood concepts and recommendations for good practice

F1000Research ◽  
2017 ◽  
Vol 4 ◽  
pp. 621
Author(s):  
Cyril Pernet
Keyword(s):  
Social Sciences ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Good Practice ◽  
Significance Testing ◽  
P Value ◽  
Null Hypothesis Significance Testing ◽  
Reporting Practices ◽  
Interpretation Errors ◽  
Test Of Significance

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short guide, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose simple reporting practices.

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